instins— title: “time_series” author: “hy” date: “April 6, 2018” output: html_document —
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library(ggplot2)
library(tidyverse)## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr## Conflicts with tidy packages ----------------------------------------------## filter(): dplyr, stats
## lag(): dplyr, statslibrary(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layoutlibrary(data.table)##
## Attaching package: 'data.table'## The following objects are masked from 'package:dplyr':
##
## between, first, last## The following object is masked from 'package:purrr':
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## transposelibrary(dygraphs)
library(quantmod)## Loading required package: xts## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric##
## Attaching package: 'xts'## The following objects are masked from 'package:data.table':
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## first, last## The following objects are masked from 'package:dplyr':
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## first, last## Loading required package: TTR## Version 0.4-0 included new data defaults. See ?getSymbols.library(fpp)## Loading required package: forecast## Loading required package: fma## Loading required package: expsmooth## Loading required package: lmtest## Loading required package: tserieslibrary(xts)
library(plyr)## -------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## -------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following object is masked from 'package:fma':
##
## ozone## The following objects are masked from 'package:plotly':
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## arrange, mutate, rename, summarise## The following objects are masked from 'package:dplyr':
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## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following object is masked from 'package:purrr':
##
## compactall_metro <- read.csv("Metro_MedianRentalPrice_AllHomes.csv", stringsAsFactors = FALSE)
specific <- c("New York, NY", "Los Angeles, CA", "Chicago, IL", "Dallas, TX", "Philadelphia, PA", "Houston, TX",
"Washington, DC", "Miami, FL", "Atlanta, GA", "San Francisco, CA", "Boston, MA", "Detroit, MI",
"Phoenix, AZ", "Seattle, WA", "Minneapolis, MN", "Austin, TX", "San Jose, CA", "Denver, CO")
metro <- all_metro[all_metro$RegionName %in% specific,]
# transpose
tmetro <- transpose(metro)
# get row and colnames in order
colnames(tmetro) <- rownames(metro)
rownames(tmetro) <- colnames(metro)
setDT(tmetro, keep.rownames = TRUE)[]## rn 2 3 4 5
## 1: RegionName New York, NY Los Angeles, CA Chicago, IL Dallas, TX
## 2: SizeRank 1 2 3 4
## 3: X2010.01 2150 NA NA NA
## 4: X2010.02 2000 2495 1550 1250
## 5: X2010.03 2300 2400 1500 1300
## 6: X2010.04 2500 2462.5 1500 1400
## 7: X2010.05 2400 2500 1500 1350
## 8: X2010.06 2650 2500 1500 1350
## 9: X2010.07 2495 2699.5 1550 1350
## 10: X2010.08 2300 2800 1500 1350
## 11: X2010.09 2300 2600 1500 1375
## 12: X2010.10 2500 2500 1560 1400
## 13: X2010.11 2500 2500 1575 1395
## 14: X2010.12 2800 2500 1600 1400
## 15: X2011.01 2500 2495 1550 1375
## 16: X2011.02 2400 2450 1525 1300
## 17: X2011.03 2500 2450 1500 1300
## 18: X2011.04 2700 2500 1550 1325
## 19: X2011.05 2700 2500 1585 1350
## 20: X2011.06 2700 2500 1600 1350
## 21: X2011.07 2700 2500 1600 1350
## 22: X2011.08 2700 2500 1600 1300
## 23: X2011.09 2600 2500 1600 1295
## 24: X2011.10 2500 2400 1550 1250
## 25: X2011.11 2500 2300 1500 1250
## 26: X2011.12 2500 2300 1500 1250
## 27: X2012.01 2400 2300 1500 1250
## 28: X2012.02 2535 2350 1500 1295
## 29: X2012.03 2500 2300 1550 1300
## 30: X2012.04 2500 2295 1500 1200
## 31: X2012.05 2595 2350 1500 1250
## 32: X2012.06 2550 2350 1550 1295
## 33: X2012.07 2599 2300 1550 1257
## 34: X2012.08 2575 2250 1550 1250
## 35: X2012.09 2595 2300 1500 1250
## 36: X2012.10 2600 2200 1500 1250
## 37: X2012.11 2600 2150 1500 1273.5
## 38: X2012.12 2750 2100 1500 1250
## 39: X2013.01 2700 2175 1500 1225
## 40: X2013.02 2750 2200 1500 1250
## 41: X2013.03 2750 2150 1500 1250
## 42: X2013.04 2800 2200 1500 1250
## 43: X2013.05 2850 2200 1550 1275
## 44: X2013.06 2800 2250 1550 1350
## 45: X2013.07 2695 2200 1582.5 1350
## 46: X2013.08 2700 2050 1595 1300
## 47: X2013.09 2700 2300 1596.5 1345
## 48: X2013.10 2600 2400 1600 1350
## 49: X2013.11 2650 2300 1595 1325
## 50: X2013.12 2750 2450 1600 1350
## 51: X2014.01 2775 2400 1595 1350
## 52: X2014.02 2750 2400 1557 1350
## 53: X2014.03 2700 2400 1550 1350
## 54: X2014.04 2675 2400 1550 1350
## 55: X2014.05 2800 2500 1600 1400
## 56: X2014.06 2850 2550 1650 1400
## 57: X2014.07 2750 2600 1650 1450
## 58: X2014.08 2700 2600 1650 1450
## 59: X2014.09 2650 2600 1650 1445
## 60: X2014.10 2600 2600 1650 1424
## 61: X2014.11 2600 2600 1600 1445
## 62: X2014.12 2650 2650 1609 1450
## 63: X2015.01 2795 2650 1600 1450
## 64: X2015.02 2795 2650 1625 1450
## 65: X2015.03 2800 2685 1649 1495
## 66: X2015.04 2900 2750 1650 1500
## 67: X2015.05 2800 2795 1650 1500
## 68: X2015.06 2850 2800 1650 1500
## 69: X2015.07 2900 2800 1665 1550
## 70: X2015.08 2900 2850 1675 1525
## 71: X2015.09 2900 2800 1650 1500
## 72: X2015.10 2800 2800 1609 1500
## 73: X2015.11 2850 2800 1645 1500
## 74: X2015.12 2900 2800 1600 1500
## 75: X2016.01 2950 2800 1605 1500
## 76: X2016.02 2899 2875 1645 1500
## 77: X2016.03 2950 2900 1650 1550
## 78: X2016.04 3000 2850 1650 1550
## 79: X2016.05 3000 2800 1650 1550
## 80: X2016.06 2900 2895 1650 1575
## 81: X2016.07 2800 2850 1650 1595
## 82: X2016.08 2700 2800 1650 1595
## 83: X2016.09 2800 2870 1600 1545
## 84: X2016.10 2800 2790 1600 1525
## 85: X2016.11 2750 2900 1600 1595
## 86: X2016.12 2700 2750 1600 1525
## 87: X2017.01 2700 2850 1600 1595
## 88: X2017.02 2750 2950 1600 1599
## 89: X2017.03 2850 3000 1625 1625
## 90: X2017.04 2975 3050 1650 1675
## 91: X2017.05 3000 3100 1695 1695
## 92: X2017.06 3000 3100 1700 1700
## 93: X2017.07 2950 3100 1700 1700
## 94: X2017.08 2895 3095 1700 1695
## 95: X2017.09 3195 3000 1695 1695
## 96: X2017.10 2950 3000 1650 1650
## 97: X2017.11 2950 3000 1650 1650
## 98: X2017.12 3000 3000 1650 1650
## 99: X2018.01 2950 3000 1650 1650
## 100: X2018.02 3000 3000 1650 1650
## rn 2 3 4 5
## 6 7 8 9 10
## 1: Philadelphia, PA Houston, TX Washington, DC Miami, FL Atlanta, GA
## 2: 5 6 7 8 9
## 3: NA NA NA NA 1012.5
## 4: 1500 NA 1650 NA 1150
## 5: 1500 NA 1700 1800 1195
## 6: 1497 NA 1750 1800 1200
## 7: 1500 NA 1750 1800 1200
## 8: 1537.5 NA 1800 1800 1200
## 9: 1500 NA 1900 1900 1200
## 10: 1500 NA 1875 1900 1200
## 11: 1500 NA 1800 1900 1200
## 12: 1495 NA 1800 1800 1200
## 13: 1500 NA 1800 1750 1200
## 14: 1500 NA 1800 1800 1200
## 15: 1400 NA 1800 1700 1175
## 16: 1400 NA 1800 1650 1150
## 17: 1400 NA 1800 1650 1150
## 18: 1500 NA 1845 1699.5 1195
## 19: 1500 NA 1900 1700 1200
## 20: 1500 NA 1950 1700 1200
## 21: 1500 NA 1945 1700 1150
## 22: 1500 NA 1900 1700 1150
## 23: 1500 NA 1900 1700 1100
## 24: 1450 NA 1900 1700 1100
## 25: 1400 NA 1850 1700 1099
## 26: 1400 NA 1850 1675 1100
## 27: 1400 NA 1825 1650 1050
## 28: 1400 NA 1800 1650 1050
## 29: 1450 NA 1850 1697 1085
## 30: 1450 NA 1895 1650 1040
## 31: 1450 NA 1925 1650 1050
## 32: 1450 NA 1990 1700 1095
## 33: 1400 NA 1975 1690 1095
## 34: 1400 NA 1950 1695 1050
## 35: 1375 NA 1900 1650 1075
## 36: 1380 NA 1900 1650 1050
## 37: 1375 NA 1900 1650 1000
## 38: 1350 NA 1899 1650 1000
## 39: 1350 NA 1895 1650 1000
## 40: 1350 NA 1900 1650 995
## 41: 1375 NA 1900 1650 1015
## 42: 1400 NA 1900 1695 1045
## 43: 1450 NA 1900 1700 1095
## 44: 1400 NA 1995 1750 1095
## 45: 1400 NA 1975 1800 1095
## 46: 1400 NA 1960 1800 1050
## 47: 1400 NA 1950 1800 1095
## 48: 1425 NA 1950 1850 1100
## 49: 1400 1375 1950 1875 1090
## 50: 1400 1395 1900 1900 1100
## 51: 1400 1375 1900 1850 1095
## 52: 1375 1355 1900 1850 1095
## 53: 1395 1395 1900 1800 1095
## 54: 1400 1450 1900 1800 1095
## 55: 1500 1500 1995 1850 1100
## 56: 1500 1550 2000 1850 1100
## 57: 1500 1550 2000 1875 1125
## 58: 1500 1575 2000 1850 1149
## 59: 1495 1550 2000 1850 1149
## 60: 1475 1525 1980 1850 1150
## 61: 1450 1550 1950 1850 1150
## 62: 1450 1550 1950 1850 1125
## 63: 1450 1550 1950 1850 1125
## 64: 1495 1550 1950 1850 1145
## 65: 1500 1575 1975 1900 1175
## 66: 1500 1600 1995 1900 1200
## 67: 1550 1600 2000 1950 1200
## 68: 1550 1650 2050 2000 1250
## 69: 1550 1625 2050 2000 1245
## 70: 1500 1600 2050 2000 1200
## 71: 1500 1600 2000 1995 1200
## 72: 1500 1600 1999 2000 1200
## 73: 1500 1600 1995 2000 1200
## 74: 1475 1600 1995 2000 1200
## 75: 1495 1600 1995 2000 1225
## 76: 1500 1600 1999 2000 1240
## 77: 1500 1645 2000 2000 1250
## 78: 1550 1650 2000 2000 1260
## 79: 1550 1600 2050 2000 1300
## 80: 1525 1600 2050 2000 1300
## 81: 1500 1575 2100 1950 1300
## 82: 1500 1585 2099 1900 1295
## 83: 1500 1556.5 2000 1950 1295
## 84: 1450 1500 2000 1950 1300
## 85: 1495 1505 1999 2000 1300
## 86: 1450 1495 1999 1875 1295
## 87: 1450 1525 2000 1930 1300
## 88: 1450 1550 2000 1950 1345
## 89: 1500 1550 2000 1975 1350
## 90: 1550 1595 2100 1995 1400
## 91: 1550 1600 2150 2000 1400
## 92: 1550 1600 2195 2000 1418.5
## 93: 1550 1600 2200 2000 1445
## 94: 1500 1600 2150 2000 1400
## 95: 1500 1600 2100 2000 1400
## 96: 1500 1595 2050 2000 1395
## 97: 1500 1590 2000 2000 1395
## 98: 1500 1595 2000 2000 1395
## 99: 1500 1575 2000 2000 1400
## 100: 1500 1591 2000 2000 1400
## 6 7 8 9 10
## 11 12 13 15 16
## 1: Boston, MA San Francisco, CA Detroit, MI Phoenix, AZ Seattle, WA
## 2: 10 11 12 14 15
## 3: NA 2600 NA NA 1200
## 4: NA 2250 NA NA 1395
## 5: 1375 2200 NA 1500 1495
## 6: 1500 2250 NA 1495 1500
## 7: 1475 2600 NA 1400 1500
## 8: 1552.5 2500 NA 1350 1595
## 9: 1597.5 2575 NA 1300 1600
## 10: 1600 2397.5 NA 1300 1685
## 11: 1600 2150 NA 1295 1600
## 12: 1600 2150 NA 1275 1595
## 13: 1691 2100 NA 1275 1550
## 14: 1695 2200 NA 1250 1499.5
## 15: 1747.5 2000 NA 1195 1550
## 16: 1707.5 2050 NA 1175 1500
## 17: 1747.5 2095 NA 1150 1500
## 18: 1850 2100 NA 1195 1525
## 19: 1900 2075 NA 1195 1595
## 20: 1900 2100 NA 1175 1595
## 21: 1975 2167.5 NA 1125 1595
## 22: 1900 2150 NA 1100 1550
## 23: 1900 2150 NA 1100 1550
## 24: 1837.5 2150 NA 1100 1500
## 25: 1850 2150 800 1095 1450
## 26: 1850 2050 850 1095 1401.5
## 27: 1900 2000 850 1099 1400
## 28: 1950 2095 850 1095 1400
## 29: 2000 2200 850 1100 1450
## 30: 2000 1995 850 1100 1450
## 31: 2050 1950 850 1120 1475
## 32: 2100 2050 850 1150 1495
## 33: 2100 2000 850 1125 1495
## 34: 2000 1995 895 1100 1495
## 35: 2150 2000 875 1100 1475
## 36: 2100 2000 850 1100 1450
## 37: 2100 2000 850 1095 1407.5
## 38: 2070 2000 850 1095 1395
## 39: 2200 2000 850 1098 1395
## 40: 2200 2000 850 1095 1385
## 41: 2200 2000 850 1100 1395
## 42: 2250 1995 850 1100 1449
## 43: 2250 2000 870 1100 1495
## 44: 2297 2000 850 1100 1525
## 45: 2300 2050 875 1100 1500
## 46: 2250 2000 875 1070 1550
## 47: 2200 2200 899 1100 1595
## 48: 2325 2200 899 1100 1625
## 49: 2300 2200 900 1100 1625
## 50: 2300 2300 900 1100 1600
## 51: 2310 2295 900 1100 1595
## 52: 2300 2250 895 1100 1595
## 53: 2300 2275 895 1100 1595
## 54: 2350 2300 899 1125 1595
## 55: 2500 2500 900 1200 1650
## 56: 2500 2500 900 1200 1650
## 57: 2500 2650 900 1200 1695
## 58: 2450 2700 925 1200 1700
## 59: 2400 2700 900 1200 1695
## 60: 2365 2700 925 1200 1695
## 61: 2325 2750 925 1200 1695
## 62: 2400 2750 925 1200 1690
## 63: 2500 2700 900 1200 1675
## 64: 2500 2799.5 900 1250 1690
## 65: 2500 2850 912.5 1290 1695
## 66: 2500 2922.5 950 1299 1695
## 67: 2500 3000 950 1299 1750
## 68: 2500 3100 950 1300 1795
## 69: 2500 3200 950 1295 1850
## 70: 2500 3100 950 1295 1895
## 71: 2450 3100 950 1295 1850
## 72: 2400 3150 950 1275 1850
## 73: 2400 3150 950 1295 1850
## 74: 2499.5 3154.5 950 1300 1850
## 75: 2600 3200 950 1300 1850
## 76: 2600 3250 950 1325 1850
## 77: 2600 3300 975 1350 1850
## 78: 2550 3300 1000 1350 1850
## 79: 2500 3250 1000 1300 1900
## 80: 2600 3250 1000 1300 1975
## 81: 2500 3300 1100 1299 2000
## 82: 2500 3200 1095 1295 2000
## 83: 2500 3195 1000 1275 1995
## 84: 2450 3000 1049 1295 1950
## 85: 2400 3000 1040 1295 1949.5
## 86: 2450 2995 1000 1250 1900
## 87: 2500 3000 1000 1300 1995
## 88: 2600 3195 995 1325 2025
## 89: 2600 3200 1000 1350 2100
## 90: 2650 3300 1050 1399 2195
## 91: 2645 3400 1075 1400 2250
## 92: 2600 3400 1100 1400 2300
## 93: 2600 3400 1100 1400 2350
## 94: 2600 3400 1100 1400 2300
## 95: 2502 3300 1100 1395 2295
## 96: 2500 3295 1050 1395 2200
## 97: 2500 3200 1050 1395 2195
## 98: 2515 3200 1025 1395 2195
## 99: 2600 3200 1000 1395 2195
## 100: 2600 3200 1050 1400 2200
## 11 12 13 15 16
## 17 22 35 36
## 1: Minneapolis, MN Denver, CO San Jose, CA Austin, TX
## 2: 16 21 34 35
## 3: NA NA NA NA
## 4: NA NA NA NA
## 5: NA NA NA NA
## 6: NA NA NA NA
## 7: NA 1672.5 NA NA
## 8: NA 1392.5 NA NA
## 9: NA 1450 NA NA
## 10: NA 1390 NA NA
## 11: 1400 1297.5 NA NA
## 12: 1400 1250 NA NA
## 13: 1399 1350 NA NA
## 14: 1450 1300 NA NA
## 15: 1300 1297.5 NA NA
## 16: 1350 1300 2195 NA
## 17: 1350 1350 2295 NA
## 18: 1395 1350 2380 NA
## 19: 1385 1375 2380 NA
## 20: 1377 1350 2400 NA
## 21: 1395 1350 2500 NA
## 22: 1395 1350 2500 NA
## 23: 1390 1355 2495 NA
## 24: 1350 1350 2400 NA
## 25: 1300 1300 2395 NA
## 26: 1295 1295 2300 NA
## 27: 1295 1295 2250 NA
## 28: 1300 1295 2275 NA
## 29: 1300 1300 2300 NA
## 30: 1300 1395 2295 NA
## 31: 1300 1395 2250 NA
## 32: 1350 1400 2300 NA
## 33: 1325 1400 2400 NA
## 34: 1300 1400 2350 NA
## 35: 1375 1400 2350 NA
## 36: 1375 1375 2400 NA
## 37: 1300 1325 2300 NA
## 38: 1350 1310 2347 NA
## 39: 1350 1350 2370 NA
## 40: 1350 1350 2300 1100
## 41: 1350 1390.5 2295.5 1024
## 42: 1350 1450 2349 1090
## 43: 1350 1395 2400 1115
## 44: 1350 1395 2400 1281.5
## 45: 1300 1500 2500 1150
## 46: 1315 1475 2431 1199
## 47: 1375 1500 2650 1285
## 48: 1395 1575 2780 1349
## 49: 1400 1595 2775 1350
## 50: 1400 1600 2800 1326
## 51: 1400 1595 2695 1325
## 52: 1400 1575 2650 1350
## 53: 1395 1550 2650 1395
## 54: 1395 1595 2700 1419.5
## 55: 1395 1600 2995 1400
## 56: 1400 1695 3000 1400
## 57: 1400 1695 3095 1450
## 58: 1400 1700 3200 1450
## 59: 1400 1790 3150 1418
## 60: 1399 1754.5 3050 1400
## 61: 1399 1789 3072.5 1395
## 62: 1400 1750 3000 1395
## 63: 1400 1789 3000 1399
## 64: 1399 1800 3100 1400
## 65: 1400 1845 3200 1400
## 66: 1425 1850 3200 1450
## 67: 1450 1895 3352.5 1495
## 68: 1456.5 1895 3500 1500
## 69: 1450 1895 3500 1525
## 70: 1450 1929 3500 1525
## 71: 1450 1900 3500 1525
## 72: 1450 1895 3495 1532.5
## 73: 1450 1850 3400 1529
## 74: 1450 1850 3300 1500
## 75: 1475 1895 3395 1500
## 76: 1475 1895 3495 1500
## 77: 1480 1900 3450 1500
## 78: 1450 1890 3400 1450
## 79: 1450 1900 3500 1450
## 80: 1475 1900 3495 1495
## 81: 1495 1895 3500 1499
## 82: 1450 1900 3400 1499
## 83: 1450 1850 3300 1445
## 84: 1450 1850 3292.5 1400
## 85: 1495 1800 3200 1400
## 86: 1450 1800 3200 1400
## 87: 1497 1850 3250 1500
## 88: 1500 1895 3300 1550
## 89: 1525 1900 3399.5 1595
## 90: 1529.5 1950 3450 1625
## 91: 1575 1995 3500 1650
## 92: 1579 1995 3600 1650
## 93: 1595 1995 3600 1650
## 94: 1595 1950 3500 1625
## 95: 1595 1950 3500 1600
## 96: 1563 1950 3480 1600
## 97: 1600 1950 3400 1595
## 98: 1600 1950 3450 1595
## 99: 1600 1950 3480 1595
## 100: 1600 1995 3485 1600
## 17 22 35 36colnames(tmetro)[1] <- "date"
names(tmetro) <- as.matrix(tmetro[1, ])
tmetro <- tmetro[-1, ]
tmetro[] <- lapply(tmetro, function(x) type.convert(as.character(x)))
tmetro <- tail(tmetro,-1)
tmetro$RegionName <- substr(tmetro$RegionName,2,8)
tmetro$RegionName <- paste(paste(substr(tmetro$RegionName,1,4), "-"),substr(tmetro$RegionName,6,7) )
tmetro$RegionName <- gsub(" ", "", tmetro$RegionName, fixed = TRUE)
tmetro$RegionName <- paste(tmetro$RegionName, "-01", sep = '')
tmetro$RegionName <- as.Date(as.character(tmetro$RegionName,"%Y-%m-%d"))
tmetro## RegionName New York, NY Los Angeles, CA Chicago, IL Dallas, TX
## 1: 2010-01-01 2150 NA NA NA
## 2: 2010-02-01 2000 2495.0 1550.0 1250.0
## 3: 2010-03-01 2300 2400.0 1500.0 1300.0
## 4: 2010-04-01 2500 2462.5 1500.0 1400.0
## 5: 2010-05-01 2400 2500.0 1500.0 1350.0
## 6: 2010-06-01 2650 2500.0 1500.0 1350.0
## 7: 2010-07-01 2495 2699.5 1550.0 1350.0
## 8: 2010-08-01 2300 2800.0 1500.0 1350.0
## 9: 2010-09-01 2300 2600.0 1500.0 1375.0
## 10: 2010-10-01 2500 2500.0 1560.0 1400.0
## 11: 2010-11-01 2500 2500.0 1575.0 1395.0
## 12: 2010-12-01 2800 2500.0 1600.0 1400.0
## 13: 2011-01-01 2500 2495.0 1550.0 1375.0
## 14: 2011-02-01 2400 2450.0 1525.0 1300.0
## 15: 2011-03-01 2500 2450.0 1500.0 1300.0
## 16: 2011-04-01 2700 2500.0 1550.0 1325.0
## 17: 2011-05-01 2700 2500.0 1585.0 1350.0
## 18: 2011-06-01 2700 2500.0 1600.0 1350.0
## 19: 2011-07-01 2700 2500.0 1600.0 1350.0
## 20: 2011-08-01 2700 2500.0 1600.0 1300.0
## 21: 2011-09-01 2600 2500.0 1600.0 1295.0
## 22: 2011-10-01 2500 2400.0 1550.0 1250.0
## 23: 2011-11-01 2500 2300.0 1500.0 1250.0
## 24: 2011-12-01 2500 2300.0 1500.0 1250.0
## 25: 2012-01-01 2400 2300.0 1500.0 1250.0
## 26: 2012-02-01 2535 2350.0 1500.0 1295.0
## 27: 2012-03-01 2500 2300.0 1550.0 1300.0
## 28: 2012-04-01 2500 2295.0 1500.0 1200.0
## 29: 2012-05-01 2595 2350.0 1500.0 1250.0
## 30: 2012-06-01 2550 2350.0 1550.0 1295.0
## 31: 2012-07-01 2599 2300.0 1550.0 1257.0
## 32: 2012-08-01 2575 2250.0 1550.0 1250.0
## 33: 2012-09-01 2595 2300.0 1500.0 1250.0
## 34: 2012-10-01 2600 2200.0 1500.0 1250.0
## 35: 2012-11-01 2600 2150.0 1500.0 1273.5
## 36: 2012-12-01 2750 2100.0 1500.0 1250.0
## 37: 2013-01-01 2700 2175.0 1500.0 1225.0
## 38: 2013-02-01 2750 2200.0 1500.0 1250.0
## 39: 2013-03-01 2750 2150.0 1500.0 1250.0
## 40: 2013-04-01 2800 2200.0 1500.0 1250.0
## 41: 2013-05-01 2850 2200.0 1550.0 1275.0
## 42: 2013-06-01 2800 2250.0 1550.0 1350.0
## 43: 2013-07-01 2695 2200.0 1582.5 1350.0
## 44: 2013-08-01 2700 2050.0 1595.0 1300.0
## 45: 2013-09-01 2700 2300.0 1596.5 1345.0
## 46: 2013-10-01 2600 2400.0 1600.0 1350.0
## 47: 2013-11-01 2650 2300.0 1595.0 1325.0
## 48: 2013-12-01 2750 2450.0 1600.0 1350.0
## 49: 2014-01-01 2775 2400.0 1595.0 1350.0
## 50: 2014-02-01 2750 2400.0 1557.0 1350.0
## 51: 2014-03-01 2700 2400.0 1550.0 1350.0
## 52: 2014-04-01 2675 2400.0 1550.0 1350.0
## 53: 2014-05-01 2800 2500.0 1600.0 1400.0
## 54: 2014-06-01 2850 2550.0 1650.0 1400.0
## 55: 2014-07-01 2750 2600.0 1650.0 1450.0
## 56: 2014-08-01 2700 2600.0 1650.0 1450.0
## 57: 2014-09-01 2650 2600.0 1650.0 1445.0
## 58: 2014-10-01 2600 2600.0 1650.0 1424.0
## 59: 2014-11-01 2600 2600.0 1600.0 1445.0
## 60: 2014-12-01 2650 2650.0 1609.0 1450.0
## 61: 2015-01-01 2795 2650.0 1600.0 1450.0
## 62: 2015-02-01 2795 2650.0 1625.0 1450.0
## 63: 2015-03-01 2800 2685.0 1649.0 1495.0
## 64: 2015-04-01 2900 2750.0 1650.0 1500.0
## 65: 2015-05-01 2800 2795.0 1650.0 1500.0
## 66: 2015-06-01 2850 2800.0 1650.0 1500.0
## 67: 2015-07-01 2900 2800.0 1665.0 1550.0
## 68: 2015-08-01 2900 2850.0 1675.0 1525.0
## 69: 2015-09-01 2900 2800.0 1650.0 1500.0
## 70: 2015-10-01 2800 2800.0 1609.0 1500.0
## 71: 2015-11-01 2850 2800.0 1645.0 1500.0
## 72: 2015-12-01 2900 2800.0 1600.0 1500.0
## 73: 2016-01-01 2950 2800.0 1605.0 1500.0
## 74: 2016-02-01 2899 2875.0 1645.0 1500.0
## 75: 2016-03-01 2950 2900.0 1650.0 1550.0
## 76: 2016-04-01 3000 2850.0 1650.0 1550.0
## 77: 2016-05-01 3000 2800.0 1650.0 1550.0
## 78: 2016-06-01 2900 2895.0 1650.0 1575.0
## 79: 2016-07-01 2800 2850.0 1650.0 1595.0
## 80: 2016-08-01 2700 2800.0 1650.0 1595.0
## 81: 2016-09-01 2800 2870.0 1600.0 1545.0
## 82: 2016-10-01 2800 2790.0 1600.0 1525.0
## 83: 2016-11-01 2750 2900.0 1600.0 1595.0
## 84: 2016-12-01 2700 2750.0 1600.0 1525.0
## 85: 2017-01-01 2700 2850.0 1600.0 1595.0
## 86: 2017-02-01 2750 2950.0 1600.0 1599.0
## 87: 2017-03-01 2850 3000.0 1625.0 1625.0
## 88: 2017-04-01 2975 3050.0 1650.0 1675.0
## 89: 2017-05-01 3000 3100.0 1695.0 1695.0
## 90: 2017-06-01 3000 3100.0 1700.0 1700.0
## 91: 2017-07-01 2950 3100.0 1700.0 1700.0
## 92: 2017-08-01 2895 3095.0 1700.0 1695.0
## 93: 2017-09-01 3195 3000.0 1695.0 1695.0
## 94: 2017-10-01 2950 3000.0 1650.0 1650.0
## 95: 2017-11-01 2950 3000.0 1650.0 1650.0
## 96: 2017-12-01 3000 3000.0 1650.0 1650.0
## 97: 2018-01-01 2950 3000.0 1650.0 1650.0
## 98: 2018-02-01 3000 3000.0 1650.0 1650.0
## RegionName New York, NY Los Angeles, CA Chicago, IL Dallas, TX
## Philadelphia, PA Houston, TX Washington, DC Miami, FL Atlanta, GA
## 1: NA NA NA NA 1012.5
## 2: 1500.0 NA 1650 NA 1150.0
## 3: 1500.0 NA 1700 1800.0 1195.0
## 4: 1497.0 NA 1750 1800.0 1200.0
## 5: 1500.0 NA 1750 1800.0 1200.0
## 6: 1537.5 NA 1800 1800.0 1200.0
## 7: 1500.0 NA 1900 1900.0 1200.0
## 8: 1500.0 NA 1875 1900.0 1200.0
## 9: 1500.0 NA 1800 1900.0 1200.0
## 10: 1495.0 NA 1800 1800.0 1200.0
## 11: 1500.0 NA 1800 1750.0 1200.0
## 12: 1500.0 NA 1800 1800.0 1200.0
## 13: 1400.0 NA 1800 1700.0 1175.0
## 14: 1400.0 NA 1800 1650.0 1150.0
## 15: 1400.0 NA 1800 1650.0 1150.0
## 16: 1500.0 NA 1845 1699.5 1195.0
## 17: 1500.0 NA 1900 1700.0 1200.0
## 18: 1500.0 NA 1950 1700.0 1200.0
## 19: 1500.0 NA 1945 1700.0 1150.0
## 20: 1500.0 NA 1900 1700.0 1150.0
## 21: 1500.0 NA 1900 1700.0 1100.0
## 22: 1450.0 NA 1900 1700.0 1100.0
## 23: 1400.0 NA 1850 1700.0 1099.0
## 24: 1400.0 NA 1850 1675.0 1100.0
## 25: 1400.0 NA 1825 1650.0 1050.0
## 26: 1400.0 NA 1800 1650.0 1050.0
## 27: 1450.0 NA 1850 1697.0 1085.0
## 28: 1450.0 NA 1895 1650.0 1040.0
## 29: 1450.0 NA 1925 1650.0 1050.0
## 30: 1450.0 NA 1990 1700.0 1095.0
## 31: 1400.0 NA 1975 1690.0 1095.0
## 32: 1400.0 NA 1950 1695.0 1050.0
## 33: 1375.0 NA 1900 1650.0 1075.0
## 34: 1380.0 NA 1900 1650.0 1050.0
## 35: 1375.0 NA 1900 1650.0 1000.0
## 36: 1350.0 NA 1899 1650.0 1000.0
## 37: 1350.0 NA 1895 1650.0 1000.0
## 38: 1350.0 NA 1900 1650.0 995.0
## 39: 1375.0 NA 1900 1650.0 1015.0
## 40: 1400.0 NA 1900 1695.0 1045.0
## 41: 1450.0 NA 1900 1700.0 1095.0
## 42: 1400.0 NA 1995 1750.0 1095.0
## 43: 1400.0 NA 1975 1800.0 1095.0
## 44: 1400.0 NA 1960 1800.0 1050.0
## 45: 1400.0 NA 1950 1800.0 1095.0
## 46: 1425.0 NA 1950 1850.0 1100.0
## 47: 1400.0 1375.0 1950 1875.0 1090.0
## 48: 1400.0 1395.0 1900 1900.0 1100.0
## 49: 1400.0 1375.0 1900 1850.0 1095.0
## 50: 1375.0 1355.0 1900 1850.0 1095.0
## 51: 1395.0 1395.0 1900 1800.0 1095.0
## 52: 1400.0 1450.0 1900 1800.0 1095.0
## 53: 1500.0 1500.0 1995 1850.0 1100.0
## 54: 1500.0 1550.0 2000 1850.0 1100.0
## 55: 1500.0 1550.0 2000 1875.0 1125.0
## 56: 1500.0 1575.0 2000 1850.0 1149.0
## 57: 1495.0 1550.0 2000 1850.0 1149.0
## 58: 1475.0 1525.0 1980 1850.0 1150.0
## 59: 1450.0 1550.0 1950 1850.0 1150.0
## 60: 1450.0 1550.0 1950 1850.0 1125.0
## 61: 1450.0 1550.0 1950 1850.0 1125.0
## 62: 1495.0 1550.0 1950 1850.0 1145.0
## 63: 1500.0 1575.0 1975 1900.0 1175.0
## 64: 1500.0 1600.0 1995 1900.0 1200.0
## 65: 1550.0 1600.0 2000 1950.0 1200.0
## 66: 1550.0 1650.0 2050 2000.0 1250.0
## 67: 1550.0 1625.0 2050 2000.0 1245.0
## 68: 1500.0 1600.0 2050 2000.0 1200.0
## 69: 1500.0 1600.0 2000 1995.0 1200.0
## 70: 1500.0 1600.0 1999 2000.0 1200.0
## 71: 1500.0 1600.0 1995 2000.0 1200.0
## 72: 1475.0 1600.0 1995 2000.0 1200.0
## 73: 1495.0 1600.0 1995 2000.0 1225.0
## 74: 1500.0 1600.0 1999 2000.0 1240.0
## 75: 1500.0 1645.0 2000 2000.0 1250.0
## 76: 1550.0 1650.0 2000 2000.0 1260.0
## 77: 1550.0 1600.0 2050 2000.0 1300.0
## 78: 1525.0 1600.0 2050 2000.0 1300.0
## 79: 1500.0 1575.0 2100 1950.0 1300.0
## 80: 1500.0 1585.0 2099 1900.0 1295.0
## 81: 1500.0 1556.5 2000 1950.0 1295.0
## 82: 1450.0 1500.0 2000 1950.0 1300.0
## 83: 1495.0 1505.0 1999 2000.0 1300.0
## 84: 1450.0 1495.0 1999 1875.0 1295.0
## 85: 1450.0 1525.0 2000 1930.0 1300.0
## 86: 1450.0 1550.0 2000 1950.0 1345.0
## 87: 1500.0 1550.0 2000 1975.0 1350.0
## 88: 1550.0 1595.0 2100 1995.0 1400.0
## 89: 1550.0 1600.0 2150 2000.0 1400.0
## 90: 1550.0 1600.0 2195 2000.0 1418.5
## 91: 1550.0 1600.0 2200 2000.0 1445.0
## 92: 1500.0 1600.0 2150 2000.0 1400.0
## 93: 1500.0 1600.0 2100 2000.0 1400.0
## 94: 1500.0 1595.0 2050 2000.0 1395.0
## 95: 1500.0 1590.0 2000 2000.0 1395.0
## 96: 1500.0 1595.0 2000 2000.0 1395.0
## 97: 1500.0 1575.0 2000 2000.0 1400.0
## 98: 1500.0 1591.0 2000 2000.0 1400.0
## Philadelphia, PA Houston, TX Washington, DC Miami, FL Atlanta, GA
## Boston, MA San Francisco, CA Detroit, MI Phoenix, AZ Seattle, WA
## 1: NA 2600.0 NA NA 1200.0
## 2: NA 2250.0 NA NA 1395.0
## 3: 1375.0 2200.0 NA 1500 1495.0
## 4: 1500.0 2250.0 NA 1495 1500.0
## 5: 1475.0 2600.0 NA 1400 1500.0
## 6: 1552.5 2500.0 NA 1350 1595.0
## 7: 1597.5 2575.0 NA 1300 1600.0
## 8: 1600.0 2397.5 NA 1300 1685.0
## 9: 1600.0 2150.0 NA 1295 1600.0
## 10: 1600.0 2150.0 NA 1275 1595.0
## 11: 1691.0 2100.0 NA 1275 1550.0
## 12: 1695.0 2200.0 NA 1250 1499.5
## 13: 1747.5 2000.0 NA 1195 1550.0
## 14: 1707.5 2050.0 NA 1175 1500.0
## 15: 1747.5 2095.0 NA 1150 1500.0
## 16: 1850.0 2100.0 NA 1195 1525.0
## 17: 1900.0 2075.0 NA 1195 1595.0
## 18: 1900.0 2100.0 NA 1175 1595.0
## 19: 1975.0 2167.5 NA 1125 1595.0
## 20: 1900.0 2150.0 NA 1100 1550.0
## 21: 1900.0 2150.0 NA 1100 1550.0
## 22: 1837.5 2150.0 NA 1100 1500.0
## 23: 1850.0 2150.0 800.0 1095 1450.0
## 24: 1850.0 2050.0 850.0 1095 1401.5
## 25: 1900.0 2000.0 850.0 1099 1400.0
## 26: 1950.0 2095.0 850.0 1095 1400.0
## 27: 2000.0 2200.0 850.0 1100 1450.0
## 28: 2000.0 1995.0 850.0 1100 1450.0
## 29: 2050.0 1950.0 850.0 1120 1475.0
## 30: 2100.0 2050.0 850.0 1150 1495.0
## 31: 2100.0 2000.0 850.0 1125 1495.0
## 32: 2000.0 1995.0 895.0 1100 1495.0
## 33: 2150.0 2000.0 875.0 1100 1475.0
## 34: 2100.0 2000.0 850.0 1100 1450.0
## 35: 2100.0 2000.0 850.0 1095 1407.5
## 36: 2070.0 2000.0 850.0 1095 1395.0
## 37: 2200.0 2000.0 850.0 1098 1395.0
## 38: 2200.0 2000.0 850.0 1095 1385.0
## 39: 2200.0 2000.0 850.0 1100 1395.0
## 40: 2250.0 1995.0 850.0 1100 1449.0
## 41: 2250.0 2000.0 870.0 1100 1495.0
## 42: 2297.0 2000.0 850.0 1100 1525.0
## 43: 2300.0 2050.0 875.0 1100 1500.0
## 44: 2250.0 2000.0 875.0 1070 1550.0
## 45: 2200.0 2200.0 899.0 1100 1595.0
## 46: 2325.0 2200.0 899.0 1100 1625.0
## 47: 2300.0 2200.0 900.0 1100 1625.0
## 48: 2300.0 2300.0 900.0 1100 1600.0
## 49: 2310.0 2295.0 900.0 1100 1595.0
## 50: 2300.0 2250.0 895.0 1100 1595.0
## 51: 2300.0 2275.0 895.0 1100 1595.0
## 52: 2350.0 2300.0 899.0 1125 1595.0
## 53: 2500.0 2500.0 900.0 1200 1650.0
## 54: 2500.0 2500.0 900.0 1200 1650.0
## 55: 2500.0 2650.0 900.0 1200 1695.0
## 56: 2450.0 2700.0 925.0 1200 1700.0
## 57: 2400.0 2700.0 900.0 1200 1695.0
## 58: 2365.0 2700.0 925.0 1200 1695.0
## 59: 2325.0 2750.0 925.0 1200 1695.0
## 60: 2400.0 2750.0 925.0 1200 1690.0
## 61: 2500.0 2700.0 900.0 1200 1675.0
## 62: 2500.0 2799.5 900.0 1250 1690.0
## 63: 2500.0 2850.0 912.5 1290 1695.0
## 64: 2500.0 2922.5 950.0 1299 1695.0
## 65: 2500.0 3000.0 950.0 1299 1750.0
## 66: 2500.0 3100.0 950.0 1300 1795.0
## 67: 2500.0 3200.0 950.0 1295 1850.0
## 68: 2500.0 3100.0 950.0 1295 1895.0
## 69: 2450.0 3100.0 950.0 1295 1850.0
## 70: 2400.0 3150.0 950.0 1275 1850.0
## 71: 2400.0 3150.0 950.0 1295 1850.0
## 72: 2499.5 3154.5 950.0 1300 1850.0
## 73: 2600.0 3200.0 950.0 1300 1850.0
## 74: 2600.0 3250.0 950.0 1325 1850.0
## 75: 2600.0 3300.0 975.0 1350 1850.0
## 76: 2550.0 3300.0 1000.0 1350 1850.0
## 77: 2500.0 3250.0 1000.0 1300 1900.0
## 78: 2600.0 3250.0 1000.0 1300 1975.0
## 79: 2500.0 3300.0 1100.0 1299 2000.0
## 80: 2500.0 3200.0 1095.0 1295 2000.0
## 81: 2500.0 3195.0 1000.0 1275 1995.0
## 82: 2450.0 3000.0 1049.0 1295 1950.0
## 83: 2400.0 3000.0 1040.0 1295 1949.5
## 84: 2450.0 2995.0 1000.0 1250 1900.0
## 85: 2500.0 3000.0 1000.0 1300 1995.0
## 86: 2600.0 3195.0 995.0 1325 2025.0
## 87: 2600.0 3200.0 1000.0 1350 2100.0
## 88: 2650.0 3300.0 1050.0 1399 2195.0
## 89: 2645.0 3400.0 1075.0 1400 2250.0
## 90: 2600.0 3400.0 1100.0 1400 2300.0
## 91: 2600.0 3400.0 1100.0 1400 2350.0
## 92: 2600.0 3400.0 1100.0 1400 2300.0
## 93: 2502.0 3300.0 1100.0 1395 2295.0
## 94: 2500.0 3295.0 1050.0 1395 2200.0
## 95: 2500.0 3200.0 1050.0 1395 2195.0
## 96: 2515.0 3200.0 1025.0 1395 2195.0
## 97: 2600.0 3200.0 1000.0 1395 2195.0
## 98: 2600.0 3200.0 1050.0 1400 2200.0
## Boston, MA San Francisco, CA Detroit, MI Phoenix, AZ Seattle, WA
## Minneapolis, MN Denver, CO San Jose, CA Austin, TX
## 1: NA NA NA NA
## 2: NA NA NA NA
## 3: NA NA NA NA
## 4: NA NA NA NA
## 5: NA 1672.5 NA NA
## 6: NA 1392.5 NA NA
## 7: NA 1450.0 NA NA
## 8: NA 1390.0 NA NA
## 9: 1400.0 1297.5 NA NA
## 10: 1400.0 1250.0 NA NA
## 11: 1399.0 1350.0 NA NA
## 12: 1450.0 1300.0 NA NA
## 13: 1300.0 1297.5 NA NA
## 14: 1350.0 1300.0 2195.0 NA
## 15: 1350.0 1350.0 2295.0 NA
## 16: 1395.0 1350.0 2380.0 NA
## 17: 1385.0 1375.0 2380.0 NA
## 18: 1377.0 1350.0 2400.0 NA
## 19: 1395.0 1350.0 2500.0 NA
## 20: 1395.0 1350.0 2500.0 NA
## 21: 1390.0 1355.0 2495.0 NA
## 22: 1350.0 1350.0 2400.0 NA
## 23: 1300.0 1300.0 2395.0 NA
## 24: 1295.0 1295.0 2300.0 NA
## 25: 1295.0 1295.0 2250.0 NA
## 26: 1300.0 1295.0 2275.0 NA
## 27: 1300.0 1300.0 2300.0 NA
## 28: 1300.0 1395.0 2295.0 NA
## 29: 1300.0 1395.0 2250.0 NA
## 30: 1350.0 1400.0 2300.0 NA
## 31: 1325.0 1400.0 2400.0 NA
## 32: 1300.0 1400.0 2350.0 NA
## 33: 1375.0 1400.0 2350.0 NA
## 34: 1375.0 1375.0 2400.0 NA
## 35: 1300.0 1325.0 2300.0 NA
## 36: 1350.0 1310.0 2347.0 NA
## 37: 1350.0 1350.0 2370.0 NA
## 38: 1350.0 1350.0 2300.0 1100.0
## 39: 1350.0 1390.5 2295.5 1024.0
## 40: 1350.0 1450.0 2349.0 1090.0
## 41: 1350.0 1395.0 2400.0 1115.0
## 42: 1350.0 1395.0 2400.0 1281.5
## 43: 1300.0 1500.0 2500.0 1150.0
## 44: 1315.0 1475.0 2431.0 1199.0
## 45: 1375.0 1500.0 2650.0 1285.0
## 46: 1395.0 1575.0 2780.0 1349.0
## 47: 1400.0 1595.0 2775.0 1350.0
## 48: 1400.0 1600.0 2800.0 1326.0
## 49: 1400.0 1595.0 2695.0 1325.0
## 50: 1400.0 1575.0 2650.0 1350.0
## 51: 1395.0 1550.0 2650.0 1395.0
## 52: 1395.0 1595.0 2700.0 1419.5
## 53: 1395.0 1600.0 2995.0 1400.0
## 54: 1400.0 1695.0 3000.0 1400.0
## 55: 1400.0 1695.0 3095.0 1450.0
## 56: 1400.0 1700.0 3200.0 1450.0
## 57: 1400.0 1790.0 3150.0 1418.0
## 58: 1399.0 1754.5 3050.0 1400.0
## 59: 1399.0 1789.0 3072.5 1395.0
## 60: 1400.0 1750.0 3000.0 1395.0
## 61: 1400.0 1789.0 3000.0 1399.0
## 62: 1399.0 1800.0 3100.0 1400.0
## 63: 1400.0 1845.0 3200.0 1400.0
## 64: 1425.0 1850.0 3200.0 1450.0
## 65: 1450.0 1895.0 3352.5 1495.0
## 66: 1456.5 1895.0 3500.0 1500.0
## 67: 1450.0 1895.0 3500.0 1525.0
## 68: 1450.0 1929.0 3500.0 1525.0
## 69: 1450.0 1900.0 3500.0 1525.0
## 70: 1450.0 1895.0 3495.0 1532.5
## 71: 1450.0 1850.0 3400.0 1529.0
## 72: 1450.0 1850.0 3300.0 1500.0
## 73: 1475.0 1895.0 3395.0 1500.0
## 74: 1475.0 1895.0 3495.0 1500.0
## 75: 1480.0 1900.0 3450.0 1500.0
## 76: 1450.0 1890.0 3400.0 1450.0
## 77: 1450.0 1900.0 3500.0 1450.0
## 78: 1475.0 1900.0 3495.0 1495.0
## 79: 1495.0 1895.0 3500.0 1499.0
## 80: 1450.0 1900.0 3400.0 1499.0
## 81: 1450.0 1850.0 3300.0 1445.0
## 82: 1450.0 1850.0 3292.5 1400.0
## 83: 1495.0 1800.0 3200.0 1400.0
## 84: 1450.0 1800.0 3200.0 1400.0
## 85: 1497.0 1850.0 3250.0 1500.0
## 86: 1500.0 1895.0 3300.0 1550.0
## 87: 1525.0 1900.0 3399.5 1595.0
## 88: 1529.5 1950.0 3450.0 1625.0
## 89: 1575.0 1995.0 3500.0 1650.0
## 90: 1579.0 1995.0 3600.0 1650.0
## 91: 1595.0 1995.0 3600.0 1650.0
## 92: 1595.0 1950.0 3500.0 1625.0
## 93: 1595.0 1950.0 3500.0 1600.0
## 94: 1563.0 1950.0 3480.0 1600.0
## 95: 1600.0 1950.0 3400.0 1595.0
## 96: 1600.0 1950.0 3450.0 1595.0
## 97: 1600.0 1950.0 3480.0 1595.0
## 98: 1600.0 1995.0 3485.0 1600.0
## Minneapolis, MN Denver, CO San Jose, CA Austin, TXny <- ggplot(tmetro, aes(x = as.Date(RegionName), y = tmetro$`New York, NY`, group=1)) +
geom_line() + theme_minimal()+ xlab("Date") + ylab("Rent Price in USD")+scale_x_date(date_breaks = "1 year", date_labels = "%Y")
ny
d <- melt(tmetro, id.vars="RegionName")## Warning in melt.data.table(tmetro, id.vars = "RegionName"):
## 'measure.vars' [New York, NY, Los Angeles, CA, Chicago, IL, Dallas,
## TX, ...] are not all of the same type. By order of hierarchy, the molten
## data value column will be of type 'double'. All measure variables not of
## type 'double' will be coerced to. Check DETAILS in ?melt.data.table for
## more on coercion.# Everything on the same plot
time <- ggplot(d, aes(as.Date(RegionName),value, col=variable, group=1)) +
geom_line() + theme(axis.text.x = element_text(angle = 90, hjust = 1))+
scale_x_date(date_breaks = "1 year", date_labels = "%Y")+ xlab("Date") + ylab("Rent Price in USD")+ theme_minimal()
ggplotly(time)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`mtime <-time +
facet_wrap(~variable)+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())+ xlab("Date") + ylab("Rent Price in USD")
ggplotly(mtime)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`mmetro <- tmetro
mmetro## RegionName New York, NY Los Angeles, CA Chicago, IL Dallas, TX
## 1: 2010-01-01 2150 NA NA NA
## 2: 2010-02-01 2000 2495.0 1550.0 1250.0
## 3: 2010-03-01 2300 2400.0 1500.0 1300.0
## 4: 2010-04-01 2500 2462.5 1500.0 1400.0
## 5: 2010-05-01 2400 2500.0 1500.0 1350.0
## 6: 2010-06-01 2650 2500.0 1500.0 1350.0
## 7: 2010-07-01 2495 2699.5 1550.0 1350.0
## 8: 2010-08-01 2300 2800.0 1500.0 1350.0
## 9: 2010-09-01 2300 2600.0 1500.0 1375.0
## 10: 2010-10-01 2500 2500.0 1560.0 1400.0
## 11: 2010-11-01 2500 2500.0 1575.0 1395.0
## 12: 2010-12-01 2800 2500.0 1600.0 1400.0
## 13: 2011-01-01 2500 2495.0 1550.0 1375.0
## 14: 2011-02-01 2400 2450.0 1525.0 1300.0
## 15: 2011-03-01 2500 2450.0 1500.0 1300.0
## 16: 2011-04-01 2700 2500.0 1550.0 1325.0
## 17: 2011-05-01 2700 2500.0 1585.0 1350.0
## 18: 2011-06-01 2700 2500.0 1600.0 1350.0
## 19: 2011-07-01 2700 2500.0 1600.0 1350.0
## 20: 2011-08-01 2700 2500.0 1600.0 1300.0
## 21: 2011-09-01 2600 2500.0 1600.0 1295.0
## 22: 2011-10-01 2500 2400.0 1550.0 1250.0
## 23: 2011-11-01 2500 2300.0 1500.0 1250.0
## 24: 2011-12-01 2500 2300.0 1500.0 1250.0
## 25: 2012-01-01 2400 2300.0 1500.0 1250.0
## 26: 2012-02-01 2535 2350.0 1500.0 1295.0
## 27: 2012-03-01 2500 2300.0 1550.0 1300.0
## 28: 2012-04-01 2500 2295.0 1500.0 1200.0
## 29: 2012-05-01 2595 2350.0 1500.0 1250.0
## 30: 2012-06-01 2550 2350.0 1550.0 1295.0
## 31: 2012-07-01 2599 2300.0 1550.0 1257.0
## 32: 2012-08-01 2575 2250.0 1550.0 1250.0
## 33: 2012-09-01 2595 2300.0 1500.0 1250.0
## 34: 2012-10-01 2600 2200.0 1500.0 1250.0
## 35: 2012-11-01 2600 2150.0 1500.0 1273.5
## 36: 2012-12-01 2750 2100.0 1500.0 1250.0
## 37: 2013-01-01 2700 2175.0 1500.0 1225.0
## 38: 2013-02-01 2750 2200.0 1500.0 1250.0
## 39: 2013-03-01 2750 2150.0 1500.0 1250.0
## 40: 2013-04-01 2800 2200.0 1500.0 1250.0
## 41: 2013-05-01 2850 2200.0 1550.0 1275.0
## 42: 2013-06-01 2800 2250.0 1550.0 1350.0
## 43: 2013-07-01 2695 2200.0 1582.5 1350.0
## 44: 2013-08-01 2700 2050.0 1595.0 1300.0
## 45: 2013-09-01 2700 2300.0 1596.5 1345.0
## 46: 2013-10-01 2600 2400.0 1600.0 1350.0
## 47: 2013-11-01 2650 2300.0 1595.0 1325.0
## 48: 2013-12-01 2750 2450.0 1600.0 1350.0
## 49: 2014-01-01 2775 2400.0 1595.0 1350.0
## 50: 2014-02-01 2750 2400.0 1557.0 1350.0
## 51: 2014-03-01 2700 2400.0 1550.0 1350.0
## 52: 2014-04-01 2675 2400.0 1550.0 1350.0
## 53: 2014-05-01 2800 2500.0 1600.0 1400.0
## 54: 2014-06-01 2850 2550.0 1650.0 1400.0
## 55: 2014-07-01 2750 2600.0 1650.0 1450.0
## 56: 2014-08-01 2700 2600.0 1650.0 1450.0
## 57: 2014-09-01 2650 2600.0 1650.0 1445.0
## 58: 2014-10-01 2600 2600.0 1650.0 1424.0
## 59: 2014-11-01 2600 2600.0 1600.0 1445.0
## 60: 2014-12-01 2650 2650.0 1609.0 1450.0
## 61: 2015-01-01 2795 2650.0 1600.0 1450.0
## 62: 2015-02-01 2795 2650.0 1625.0 1450.0
## 63: 2015-03-01 2800 2685.0 1649.0 1495.0
## 64: 2015-04-01 2900 2750.0 1650.0 1500.0
## 65: 2015-05-01 2800 2795.0 1650.0 1500.0
## 66: 2015-06-01 2850 2800.0 1650.0 1500.0
## 67: 2015-07-01 2900 2800.0 1665.0 1550.0
## 68: 2015-08-01 2900 2850.0 1675.0 1525.0
## 69: 2015-09-01 2900 2800.0 1650.0 1500.0
## 70: 2015-10-01 2800 2800.0 1609.0 1500.0
## 71: 2015-11-01 2850 2800.0 1645.0 1500.0
## 72: 2015-12-01 2900 2800.0 1600.0 1500.0
## 73: 2016-01-01 2950 2800.0 1605.0 1500.0
## 74: 2016-02-01 2899 2875.0 1645.0 1500.0
## 75: 2016-03-01 2950 2900.0 1650.0 1550.0
## 76: 2016-04-01 3000 2850.0 1650.0 1550.0
## 77: 2016-05-01 3000 2800.0 1650.0 1550.0
## 78: 2016-06-01 2900 2895.0 1650.0 1575.0
## 79: 2016-07-01 2800 2850.0 1650.0 1595.0
## 80: 2016-08-01 2700 2800.0 1650.0 1595.0
## 81: 2016-09-01 2800 2870.0 1600.0 1545.0
## 82: 2016-10-01 2800 2790.0 1600.0 1525.0
## 83: 2016-11-01 2750 2900.0 1600.0 1595.0
## 84: 2016-12-01 2700 2750.0 1600.0 1525.0
## 85: 2017-01-01 2700 2850.0 1600.0 1595.0
## 86: 2017-02-01 2750 2950.0 1600.0 1599.0
## 87: 2017-03-01 2850 3000.0 1625.0 1625.0
## 88: 2017-04-01 2975 3050.0 1650.0 1675.0
## 89: 2017-05-01 3000 3100.0 1695.0 1695.0
## 90: 2017-06-01 3000 3100.0 1700.0 1700.0
## 91: 2017-07-01 2950 3100.0 1700.0 1700.0
## 92: 2017-08-01 2895 3095.0 1700.0 1695.0
## 93: 2017-09-01 3195 3000.0 1695.0 1695.0
## 94: 2017-10-01 2950 3000.0 1650.0 1650.0
## 95: 2017-11-01 2950 3000.0 1650.0 1650.0
## 96: 2017-12-01 3000 3000.0 1650.0 1650.0
## 97: 2018-01-01 2950 3000.0 1650.0 1650.0
## 98: 2018-02-01 3000 3000.0 1650.0 1650.0
## RegionName New York, NY Los Angeles, CA Chicago, IL Dallas, TX
## Philadelphia, PA Houston, TX Washington, DC Miami, FL Atlanta, GA
## 1: NA NA NA NA 1012.5
## 2: 1500.0 NA 1650 NA 1150.0
## 3: 1500.0 NA 1700 1800.0 1195.0
## 4: 1497.0 NA 1750 1800.0 1200.0
## 5: 1500.0 NA 1750 1800.0 1200.0
## 6: 1537.5 NA 1800 1800.0 1200.0
## 7: 1500.0 NA 1900 1900.0 1200.0
## 8: 1500.0 NA 1875 1900.0 1200.0
## 9: 1500.0 NA 1800 1900.0 1200.0
## 10: 1495.0 NA 1800 1800.0 1200.0
## 11: 1500.0 NA 1800 1750.0 1200.0
## 12: 1500.0 NA 1800 1800.0 1200.0
## 13: 1400.0 NA 1800 1700.0 1175.0
## 14: 1400.0 NA 1800 1650.0 1150.0
## 15: 1400.0 NA 1800 1650.0 1150.0
## 16: 1500.0 NA 1845 1699.5 1195.0
## 17: 1500.0 NA 1900 1700.0 1200.0
## 18: 1500.0 NA 1950 1700.0 1200.0
## 19: 1500.0 NA 1945 1700.0 1150.0
## 20: 1500.0 NA 1900 1700.0 1150.0
## 21: 1500.0 NA 1900 1700.0 1100.0
## 22: 1450.0 NA 1900 1700.0 1100.0
## 23: 1400.0 NA 1850 1700.0 1099.0
## 24: 1400.0 NA 1850 1675.0 1100.0
## 25: 1400.0 NA 1825 1650.0 1050.0
## 26: 1400.0 NA 1800 1650.0 1050.0
## 27: 1450.0 NA 1850 1697.0 1085.0
## 28: 1450.0 NA 1895 1650.0 1040.0
## 29: 1450.0 NA 1925 1650.0 1050.0
## 30: 1450.0 NA 1990 1700.0 1095.0
## 31: 1400.0 NA 1975 1690.0 1095.0
## 32: 1400.0 NA 1950 1695.0 1050.0
## 33: 1375.0 NA 1900 1650.0 1075.0
## 34: 1380.0 NA 1900 1650.0 1050.0
## 35: 1375.0 NA 1900 1650.0 1000.0
## 36: 1350.0 NA 1899 1650.0 1000.0
## 37: 1350.0 NA 1895 1650.0 1000.0
## 38: 1350.0 NA 1900 1650.0 995.0
## 39: 1375.0 NA 1900 1650.0 1015.0
## 40: 1400.0 NA 1900 1695.0 1045.0
## 41: 1450.0 NA 1900 1700.0 1095.0
## 42: 1400.0 NA 1995 1750.0 1095.0
## 43: 1400.0 NA 1975 1800.0 1095.0
## 44: 1400.0 NA 1960 1800.0 1050.0
## 45: 1400.0 NA 1950 1800.0 1095.0
## 46: 1425.0 NA 1950 1850.0 1100.0
## 47: 1400.0 1375.0 1950 1875.0 1090.0
## 48: 1400.0 1395.0 1900 1900.0 1100.0
## 49: 1400.0 1375.0 1900 1850.0 1095.0
## 50: 1375.0 1355.0 1900 1850.0 1095.0
## 51: 1395.0 1395.0 1900 1800.0 1095.0
## 52: 1400.0 1450.0 1900 1800.0 1095.0
## 53: 1500.0 1500.0 1995 1850.0 1100.0
## 54: 1500.0 1550.0 2000 1850.0 1100.0
## 55: 1500.0 1550.0 2000 1875.0 1125.0
## 56: 1500.0 1575.0 2000 1850.0 1149.0
## 57: 1495.0 1550.0 2000 1850.0 1149.0
## 58: 1475.0 1525.0 1980 1850.0 1150.0
## 59: 1450.0 1550.0 1950 1850.0 1150.0
## 60: 1450.0 1550.0 1950 1850.0 1125.0
## 61: 1450.0 1550.0 1950 1850.0 1125.0
## 62: 1495.0 1550.0 1950 1850.0 1145.0
## 63: 1500.0 1575.0 1975 1900.0 1175.0
## 64: 1500.0 1600.0 1995 1900.0 1200.0
## 65: 1550.0 1600.0 2000 1950.0 1200.0
## 66: 1550.0 1650.0 2050 2000.0 1250.0
## 67: 1550.0 1625.0 2050 2000.0 1245.0
## 68: 1500.0 1600.0 2050 2000.0 1200.0
## 69: 1500.0 1600.0 2000 1995.0 1200.0
## 70: 1500.0 1600.0 1999 2000.0 1200.0
## 71: 1500.0 1600.0 1995 2000.0 1200.0
## 72: 1475.0 1600.0 1995 2000.0 1200.0
## 73: 1495.0 1600.0 1995 2000.0 1225.0
## 74: 1500.0 1600.0 1999 2000.0 1240.0
## 75: 1500.0 1645.0 2000 2000.0 1250.0
## 76: 1550.0 1650.0 2000 2000.0 1260.0
## 77: 1550.0 1600.0 2050 2000.0 1300.0
## 78: 1525.0 1600.0 2050 2000.0 1300.0
## 79: 1500.0 1575.0 2100 1950.0 1300.0
## 80: 1500.0 1585.0 2099 1900.0 1295.0
## 81: 1500.0 1556.5 2000 1950.0 1295.0
## 82: 1450.0 1500.0 2000 1950.0 1300.0
## 83: 1495.0 1505.0 1999 2000.0 1300.0
## 84: 1450.0 1495.0 1999 1875.0 1295.0
## 85: 1450.0 1525.0 2000 1930.0 1300.0
## 86: 1450.0 1550.0 2000 1950.0 1345.0
## 87: 1500.0 1550.0 2000 1975.0 1350.0
## 88: 1550.0 1595.0 2100 1995.0 1400.0
## 89: 1550.0 1600.0 2150 2000.0 1400.0
## 90: 1550.0 1600.0 2195 2000.0 1418.5
## 91: 1550.0 1600.0 2200 2000.0 1445.0
## 92: 1500.0 1600.0 2150 2000.0 1400.0
## 93: 1500.0 1600.0 2100 2000.0 1400.0
## 94: 1500.0 1595.0 2050 2000.0 1395.0
## 95: 1500.0 1590.0 2000 2000.0 1395.0
## 96: 1500.0 1595.0 2000 2000.0 1395.0
## 97: 1500.0 1575.0 2000 2000.0 1400.0
## 98: 1500.0 1591.0 2000 2000.0 1400.0
## Philadelphia, PA Houston, TX Washington, DC Miami, FL Atlanta, GA
## Boston, MA San Francisco, CA Detroit, MI Phoenix, AZ Seattle, WA
## 1: NA 2600.0 NA NA 1200.0
## 2: NA 2250.0 NA NA 1395.0
## 3: 1375.0 2200.0 NA 1500 1495.0
## 4: 1500.0 2250.0 NA 1495 1500.0
## 5: 1475.0 2600.0 NA 1400 1500.0
## 6: 1552.5 2500.0 NA 1350 1595.0
## 7: 1597.5 2575.0 NA 1300 1600.0
## 8: 1600.0 2397.5 NA 1300 1685.0
## 9: 1600.0 2150.0 NA 1295 1600.0
## 10: 1600.0 2150.0 NA 1275 1595.0
## 11: 1691.0 2100.0 NA 1275 1550.0
## 12: 1695.0 2200.0 NA 1250 1499.5
## 13: 1747.5 2000.0 NA 1195 1550.0
## 14: 1707.5 2050.0 NA 1175 1500.0
## 15: 1747.5 2095.0 NA 1150 1500.0
## 16: 1850.0 2100.0 NA 1195 1525.0
## 17: 1900.0 2075.0 NA 1195 1595.0
## 18: 1900.0 2100.0 NA 1175 1595.0
## 19: 1975.0 2167.5 NA 1125 1595.0
## 20: 1900.0 2150.0 NA 1100 1550.0
## 21: 1900.0 2150.0 NA 1100 1550.0
## 22: 1837.5 2150.0 NA 1100 1500.0
## 23: 1850.0 2150.0 800.0 1095 1450.0
## 24: 1850.0 2050.0 850.0 1095 1401.5
## 25: 1900.0 2000.0 850.0 1099 1400.0
## 26: 1950.0 2095.0 850.0 1095 1400.0
## 27: 2000.0 2200.0 850.0 1100 1450.0
## 28: 2000.0 1995.0 850.0 1100 1450.0
## 29: 2050.0 1950.0 850.0 1120 1475.0
## 30: 2100.0 2050.0 850.0 1150 1495.0
## 31: 2100.0 2000.0 850.0 1125 1495.0
## 32: 2000.0 1995.0 895.0 1100 1495.0
## 33: 2150.0 2000.0 875.0 1100 1475.0
## 34: 2100.0 2000.0 850.0 1100 1450.0
## 35: 2100.0 2000.0 850.0 1095 1407.5
## 36: 2070.0 2000.0 850.0 1095 1395.0
## 37: 2200.0 2000.0 850.0 1098 1395.0
## 38: 2200.0 2000.0 850.0 1095 1385.0
## 39: 2200.0 2000.0 850.0 1100 1395.0
## 40: 2250.0 1995.0 850.0 1100 1449.0
## 41: 2250.0 2000.0 870.0 1100 1495.0
## 42: 2297.0 2000.0 850.0 1100 1525.0
## 43: 2300.0 2050.0 875.0 1100 1500.0
## 44: 2250.0 2000.0 875.0 1070 1550.0
## 45: 2200.0 2200.0 899.0 1100 1595.0
## 46: 2325.0 2200.0 899.0 1100 1625.0
## 47: 2300.0 2200.0 900.0 1100 1625.0
## 48: 2300.0 2300.0 900.0 1100 1600.0
## 49: 2310.0 2295.0 900.0 1100 1595.0
## 50: 2300.0 2250.0 895.0 1100 1595.0
## 51: 2300.0 2275.0 895.0 1100 1595.0
## 52: 2350.0 2300.0 899.0 1125 1595.0
## 53: 2500.0 2500.0 900.0 1200 1650.0
## 54: 2500.0 2500.0 900.0 1200 1650.0
## 55: 2500.0 2650.0 900.0 1200 1695.0
## 56: 2450.0 2700.0 925.0 1200 1700.0
## 57: 2400.0 2700.0 900.0 1200 1695.0
## 58: 2365.0 2700.0 925.0 1200 1695.0
## 59: 2325.0 2750.0 925.0 1200 1695.0
## 60: 2400.0 2750.0 925.0 1200 1690.0
## 61: 2500.0 2700.0 900.0 1200 1675.0
## 62: 2500.0 2799.5 900.0 1250 1690.0
## 63: 2500.0 2850.0 912.5 1290 1695.0
## 64: 2500.0 2922.5 950.0 1299 1695.0
## 65: 2500.0 3000.0 950.0 1299 1750.0
## 66: 2500.0 3100.0 950.0 1300 1795.0
## 67: 2500.0 3200.0 950.0 1295 1850.0
## 68: 2500.0 3100.0 950.0 1295 1895.0
## 69: 2450.0 3100.0 950.0 1295 1850.0
## 70: 2400.0 3150.0 950.0 1275 1850.0
## 71: 2400.0 3150.0 950.0 1295 1850.0
## 72: 2499.5 3154.5 950.0 1300 1850.0
## 73: 2600.0 3200.0 950.0 1300 1850.0
## 74: 2600.0 3250.0 950.0 1325 1850.0
## 75: 2600.0 3300.0 975.0 1350 1850.0
## 76: 2550.0 3300.0 1000.0 1350 1850.0
## 77: 2500.0 3250.0 1000.0 1300 1900.0
## 78: 2600.0 3250.0 1000.0 1300 1975.0
## 79: 2500.0 3300.0 1100.0 1299 2000.0
## 80: 2500.0 3200.0 1095.0 1295 2000.0
## 81: 2500.0 3195.0 1000.0 1275 1995.0
## 82: 2450.0 3000.0 1049.0 1295 1950.0
## 83: 2400.0 3000.0 1040.0 1295 1949.5
## 84: 2450.0 2995.0 1000.0 1250 1900.0
## 85: 2500.0 3000.0 1000.0 1300 1995.0
## 86: 2600.0 3195.0 995.0 1325 2025.0
## 87: 2600.0 3200.0 1000.0 1350 2100.0
## 88: 2650.0 3300.0 1050.0 1399 2195.0
## 89: 2645.0 3400.0 1075.0 1400 2250.0
## 90: 2600.0 3400.0 1100.0 1400 2300.0
## 91: 2600.0 3400.0 1100.0 1400 2350.0
## 92: 2600.0 3400.0 1100.0 1400 2300.0
## 93: 2502.0 3300.0 1100.0 1395 2295.0
## 94: 2500.0 3295.0 1050.0 1395 2200.0
## 95: 2500.0 3200.0 1050.0 1395 2195.0
## 96: 2515.0 3200.0 1025.0 1395 2195.0
## 97: 2600.0 3200.0 1000.0 1395 2195.0
## 98: 2600.0 3200.0 1050.0 1400 2200.0
## Boston, MA San Francisco, CA Detroit, MI Phoenix, AZ Seattle, WA
## Minneapolis, MN Denver, CO San Jose, CA Austin, TX
## 1: NA NA NA NA
## 2: NA NA NA NA
## 3: NA NA NA NA
## 4: NA NA NA NA
## 5: NA 1672.5 NA NA
## 6: NA 1392.5 NA NA
## 7: NA 1450.0 NA NA
## 8: NA 1390.0 NA NA
## 9: 1400.0 1297.5 NA NA
## 10: 1400.0 1250.0 NA NA
## 11: 1399.0 1350.0 NA NA
## 12: 1450.0 1300.0 NA NA
## 13: 1300.0 1297.5 NA NA
## 14: 1350.0 1300.0 2195.0 NA
## 15: 1350.0 1350.0 2295.0 NA
## 16: 1395.0 1350.0 2380.0 NA
## 17: 1385.0 1375.0 2380.0 NA
## 18: 1377.0 1350.0 2400.0 NA
## 19: 1395.0 1350.0 2500.0 NA
## 20: 1395.0 1350.0 2500.0 NA
## 21: 1390.0 1355.0 2495.0 NA
## 22: 1350.0 1350.0 2400.0 NA
## 23: 1300.0 1300.0 2395.0 NA
## 24: 1295.0 1295.0 2300.0 NA
## 25: 1295.0 1295.0 2250.0 NA
## 26: 1300.0 1295.0 2275.0 NA
## 27: 1300.0 1300.0 2300.0 NA
## 28: 1300.0 1395.0 2295.0 NA
## 29: 1300.0 1395.0 2250.0 NA
## 30: 1350.0 1400.0 2300.0 NA
## 31: 1325.0 1400.0 2400.0 NA
## 32: 1300.0 1400.0 2350.0 NA
## 33: 1375.0 1400.0 2350.0 NA
## 34: 1375.0 1375.0 2400.0 NA
## 35: 1300.0 1325.0 2300.0 NA
## 36: 1350.0 1310.0 2347.0 NA
## 37: 1350.0 1350.0 2370.0 NA
## 38: 1350.0 1350.0 2300.0 1100.0
## 39: 1350.0 1390.5 2295.5 1024.0
## 40: 1350.0 1450.0 2349.0 1090.0
## 41: 1350.0 1395.0 2400.0 1115.0
## 42: 1350.0 1395.0 2400.0 1281.5
## 43: 1300.0 1500.0 2500.0 1150.0
## 44: 1315.0 1475.0 2431.0 1199.0
## 45: 1375.0 1500.0 2650.0 1285.0
## 46: 1395.0 1575.0 2780.0 1349.0
## 47: 1400.0 1595.0 2775.0 1350.0
## 48: 1400.0 1600.0 2800.0 1326.0
## 49: 1400.0 1595.0 2695.0 1325.0
## 50: 1400.0 1575.0 2650.0 1350.0
## 51: 1395.0 1550.0 2650.0 1395.0
## 52: 1395.0 1595.0 2700.0 1419.5
## 53: 1395.0 1600.0 2995.0 1400.0
## 54: 1400.0 1695.0 3000.0 1400.0
## 55: 1400.0 1695.0 3095.0 1450.0
## 56: 1400.0 1700.0 3200.0 1450.0
## 57: 1400.0 1790.0 3150.0 1418.0
## 58: 1399.0 1754.5 3050.0 1400.0
## 59: 1399.0 1789.0 3072.5 1395.0
## 60: 1400.0 1750.0 3000.0 1395.0
## 61: 1400.0 1789.0 3000.0 1399.0
## 62: 1399.0 1800.0 3100.0 1400.0
## 63: 1400.0 1845.0 3200.0 1400.0
## 64: 1425.0 1850.0 3200.0 1450.0
## 65: 1450.0 1895.0 3352.5 1495.0
## 66: 1456.5 1895.0 3500.0 1500.0
## 67: 1450.0 1895.0 3500.0 1525.0
## 68: 1450.0 1929.0 3500.0 1525.0
## 69: 1450.0 1900.0 3500.0 1525.0
## 70: 1450.0 1895.0 3495.0 1532.5
## 71: 1450.0 1850.0 3400.0 1529.0
## 72: 1450.0 1850.0 3300.0 1500.0
## 73: 1475.0 1895.0 3395.0 1500.0
## 74: 1475.0 1895.0 3495.0 1500.0
## 75: 1480.0 1900.0 3450.0 1500.0
## 76: 1450.0 1890.0 3400.0 1450.0
## 77: 1450.0 1900.0 3500.0 1450.0
## 78: 1475.0 1900.0 3495.0 1495.0
## 79: 1495.0 1895.0 3500.0 1499.0
## 80: 1450.0 1900.0 3400.0 1499.0
## 81: 1450.0 1850.0 3300.0 1445.0
## 82: 1450.0 1850.0 3292.5 1400.0
## 83: 1495.0 1800.0 3200.0 1400.0
## 84: 1450.0 1800.0 3200.0 1400.0
## 85: 1497.0 1850.0 3250.0 1500.0
## 86: 1500.0 1895.0 3300.0 1550.0
## 87: 1525.0 1900.0 3399.5 1595.0
## 88: 1529.5 1950.0 3450.0 1625.0
## 89: 1575.0 1995.0 3500.0 1650.0
## 90: 1579.0 1995.0 3600.0 1650.0
## 91: 1595.0 1995.0 3600.0 1650.0
## 92: 1595.0 1950.0 3500.0 1625.0
## 93: 1595.0 1950.0 3500.0 1600.0
## 94: 1563.0 1950.0 3480.0 1600.0
## 95: 1600.0 1950.0 3400.0 1595.0
## 96: 1600.0 1950.0 3450.0 1595.0
## 97: 1600.0 1950.0 3480.0 1595.0
## 98: 1600.0 1995.0 3485.0 1600.0
## Minneapolis, MN Denver, CO San Jose, CA Austin, TXdygraph(mmetro, main = "Rent Prices in US metro area")%>%
dyRangeSelector()%>%
dyLegend(show = 'follow')chartSeries(mmetro) 
lineChart(mmetro,line.type='h',TA=NULL) 
di <- d
cpi <- c(218.1,224.9,229.6,233.0, 236.7, 237.0, 240.0,244.7)
di$adj <- di$value
di$adj <- ifelse(substr(di$RegionName,1,4) == "2010", di$value*218.1/cpi[1], di$adj)
di$adj <- ifelse(substr(di$RegionName,1,4) == "2011", di$value*218.1/cpi[2], di$adj)
di$adj <- ifelse(substr(di$RegionName,1,4) == "2012", di$value*218.1/cpi[3], di$adj)
di$adj <- ifelse(substr(di$RegionName,1,4) == "2013", di$value*218.1/cpi[4], di$adj)
di$adj <- ifelse(substr(di$RegionName,1,4) == "2014", di$value*218.1/cpi[5], di$adj)
di$adj <- ifelse(substr(di$RegionName,1,4) == "2015", di$value*218.1/cpi[6], di$adj)
di$adj <- ifelse(substr(di$RegionName,1,4) == "2016", di$value*218.1/cpi[7], di$adj)
di$adj <- ifelse(substr(di$RegionName,1,4) == "2017", di$value*218.1/cpi[8], di$adj)
di$adj <- ifelse(substr(di$RegionName,1,4) == "2018", di$value*218.1/cpi[9], di$adj)
d## RegionName variable value
## 1: 2010-01-01 New York, NY 2150
## 2: 2010-02-01 New York, NY 2000
## 3: 2010-03-01 New York, NY 2300
## 4: 2010-04-01 New York, NY 2500
## 5: 2010-05-01 New York, NY 2400
## ---
## 1760: 2017-10-01 Austin, TX 1600
## 1761: 2017-11-01 Austin, TX 1595
## 1762: 2017-12-01 Austin, TX 1595
## 1763: 2018-01-01 Austin, TX 1595
## 1764: 2018-02-01 Austin, TX 1600di## RegionName variable value adj
## 1: 2010-01-01 New York, NY 2150 2150.000
## 2: 2010-02-01 New York, NY 2000 2000.000
## 3: 2010-03-01 New York, NY 2300 2300.000
## 4: 2010-04-01 New York, NY 2500 2500.000
## 5: 2010-05-01 New York, NY 2400 2400.000
## ---
## 1760: 2017-10-01 Austin, TX 1600 1426.073
## 1761: 2017-11-01 Austin, TX 1595 1421.616
## 1762: 2017-12-01 Austin, TX 1595 1421.616
## 1763: 2018-01-01 Austin, TX 1595 NA
## 1764: 2018-02-01 Austin, TX 1600 NAdi$value <-di$adj
adj_time <- ggplot(di, aes(RegionName,value, col=variable, group=1)) +
geom_line() + theme(axis.text.x = element_text(angle = 90, hjust = 1))+
scale_x_discrete(breaks = di$RegionName[seq(1, length(di$RegionName), by = 2)])+ xlab("Date") + ylab("Rent Price in USD")+ scale_x_date(date_breaks = "1 year", date_labels = "%Y-%m-%d")+ xlab("Date")## Scale for 'x' is already present. Adding another scale for 'x', which
## will replace the existing scale.ggplotly(adj_time)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`nydata <- data.frame(rand = character(98))
nydata$date <- mmetro$RegionName
nydata$val <- mmetro$`New York, NY`
nydata$rand <- NULL
nydata## date val
## 1 2010-01-01 2150
## 2 2010-02-01 2000
## 3 2010-03-01 2300
## 4 2010-04-01 2500
## 5 2010-05-01 2400
## 6 2010-06-01 2650
## 7 2010-07-01 2495
## 8 2010-08-01 2300
## 9 2010-09-01 2300
## 10 2010-10-01 2500
## 11 2010-11-01 2500
## 12 2010-12-01 2800
## 13 2011-01-01 2500
## 14 2011-02-01 2400
## 15 2011-03-01 2500
## 16 2011-04-01 2700
## 17 2011-05-01 2700
## 18 2011-06-01 2700
## 19 2011-07-01 2700
## 20 2011-08-01 2700
## 21 2011-09-01 2600
## 22 2011-10-01 2500
## 23 2011-11-01 2500
## 24 2011-12-01 2500
## 25 2012-01-01 2400
## 26 2012-02-01 2535
## 27 2012-03-01 2500
## 28 2012-04-01 2500
## 29 2012-05-01 2595
## 30 2012-06-01 2550
## 31 2012-07-01 2599
## 32 2012-08-01 2575
## 33 2012-09-01 2595
## 34 2012-10-01 2600
## 35 2012-11-01 2600
## 36 2012-12-01 2750
## 37 2013-01-01 2700
## 38 2013-02-01 2750
## 39 2013-03-01 2750
## 40 2013-04-01 2800
## 41 2013-05-01 2850
## 42 2013-06-01 2800
## 43 2013-07-01 2695
## 44 2013-08-01 2700
## 45 2013-09-01 2700
## 46 2013-10-01 2600
## 47 2013-11-01 2650
## 48 2013-12-01 2750
## 49 2014-01-01 2775
## 50 2014-02-01 2750
## 51 2014-03-01 2700
## 52 2014-04-01 2675
## 53 2014-05-01 2800
## 54 2014-06-01 2850
## 55 2014-07-01 2750
## 56 2014-08-01 2700
## 57 2014-09-01 2650
## 58 2014-10-01 2600
## 59 2014-11-01 2600
## 60 2014-12-01 2650
## 61 2015-01-01 2795
## 62 2015-02-01 2795
## 63 2015-03-01 2800
## 64 2015-04-01 2900
## 65 2015-05-01 2800
## 66 2015-06-01 2850
## 67 2015-07-01 2900
## 68 2015-08-01 2900
## 69 2015-09-01 2900
## 70 2015-10-01 2800
## 71 2015-11-01 2850
## 72 2015-12-01 2900
## 73 2016-01-01 2950
## 74 2016-02-01 2899
## 75 2016-03-01 2950
## 76 2016-04-01 3000
## 77 2016-05-01 3000
## 78 2016-06-01 2900
## 79 2016-07-01 2800
## 80 2016-08-01 2700
## 81 2016-09-01 2800
## 82 2016-10-01 2800
## 83 2016-11-01 2750
## 84 2016-12-01 2700
## 85 2017-01-01 2700
## 86 2017-02-01 2750
## 87 2017-03-01 2850
## 88 2017-04-01 2975
## 89 2017-05-01 3000
## 90 2017-06-01 3000
## 91 2017-07-01 2950
## 92 2017-08-01 2895
## 93 2017-09-01 3195
## 94 2017-10-01 2950
## 95 2017-11-01 2950
## 96 2017-12-01 3000
## 97 2018-01-01 2950
## 98 2018-02-01 3000plot(forecast(auto.arima(nydata$val), h=100))
xnydata=xts(x = nydata$val, order.by = nydata$date)
dygraph(xnydata) %>%
dyOptions( stemPlot=TRUE)trend=nydata$val
cnydata=data.frame(time=nydata$date, open=shift(nydata$val, 1L, type="lag"), high=nydata$val+20, low=nydata$val-20, close=nydata$val)
cnydata=xts(x = cnydata[,-1], order.by = cnydata$time)
dygraph(cnydata) %>%
dyCandlestick()adj_time <- ggplot(di, aes(RegionName,value, col=variable, group=1, fill=variable)) +
geom_area() + theme(axis.text.x = element_text(angle = 90, hjust = 1))+
scale_x_discrete(breaks = di$RegionName[seq(1, length(di$RegionName), by = 2)]) + xlab("Date") + ylab("Rent Price in USD")+scale_x_date(date_breaks = "1 year", date_labels = "%Y-%m-%d")## Scale for 'x' is already present. Adding another scale for 'x', which
## will replace the existing scale.ggplotly(adj_time)## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`## Warning: Removed 177 rows containing missing values (position_stack).gnydata <- nydata
gnydata$growth <- with(gnydata, ave(val,
FUN=function(val) c(NA, diff(val)/val[-length(val)]) ))
gnydata## date val growth
## 1 2010-01-01 2150 NA
## 2 2010-02-01 2000 -0.069767442
## 3 2010-03-01 2300 0.150000000
## 4 2010-04-01 2500 0.086956522
## 5 2010-05-01 2400 -0.040000000
## 6 2010-06-01 2650 0.104166667
## 7 2010-07-01 2495 -0.058490566
## 8 2010-08-01 2300 -0.078156313
## 9 2010-09-01 2300 0.000000000
## 10 2010-10-01 2500 0.086956522
## 11 2010-11-01 2500 0.000000000
## 12 2010-12-01 2800 0.120000000
## 13 2011-01-01 2500 -0.107142857
## 14 2011-02-01 2400 -0.040000000
## 15 2011-03-01 2500 0.041666667
## 16 2011-04-01 2700 0.080000000
## 17 2011-05-01 2700 0.000000000
## 18 2011-06-01 2700 0.000000000
## 19 2011-07-01 2700 0.000000000
## 20 2011-08-01 2700 0.000000000
## 21 2011-09-01 2600 -0.037037037
## 22 2011-10-01 2500 -0.038461538
## 23 2011-11-01 2500 0.000000000
## 24 2011-12-01 2500 0.000000000
## 25 2012-01-01 2400 -0.040000000
## 26 2012-02-01 2535 0.056250000
## 27 2012-03-01 2500 -0.013806706
## 28 2012-04-01 2500 0.000000000
## 29 2012-05-01 2595 0.038000000
## 30 2012-06-01 2550 -0.017341040
## 31 2012-07-01 2599 0.019215686
## 32 2012-08-01 2575 -0.009234321
## 33 2012-09-01 2595 0.007766990
## 34 2012-10-01 2600 0.001926782
## 35 2012-11-01 2600 0.000000000
## 36 2012-12-01 2750 0.057692308
## 37 2013-01-01 2700 -0.018181818
## 38 2013-02-01 2750 0.018518519
## 39 2013-03-01 2750 0.000000000
## 40 2013-04-01 2800 0.018181818
## 41 2013-05-01 2850 0.017857143
## 42 2013-06-01 2800 -0.017543860
## 43 2013-07-01 2695 -0.037500000
## 44 2013-08-01 2700 0.001855288
## 45 2013-09-01 2700 0.000000000
## 46 2013-10-01 2600 -0.037037037
## 47 2013-11-01 2650 0.019230769
## 48 2013-12-01 2750 0.037735849
## 49 2014-01-01 2775 0.009090909
## 50 2014-02-01 2750 -0.009009009
## 51 2014-03-01 2700 -0.018181818
## 52 2014-04-01 2675 -0.009259259
## 53 2014-05-01 2800 0.046728972
## 54 2014-06-01 2850 0.017857143
## 55 2014-07-01 2750 -0.035087719
## 56 2014-08-01 2700 -0.018181818
## 57 2014-09-01 2650 -0.018518519
## 58 2014-10-01 2600 -0.018867925
## 59 2014-11-01 2600 0.000000000
## 60 2014-12-01 2650 0.019230769
## 61 2015-01-01 2795 0.054716981
## 62 2015-02-01 2795 0.000000000
## 63 2015-03-01 2800 0.001788909
## 64 2015-04-01 2900 0.035714286
## 65 2015-05-01 2800 -0.034482759
## 66 2015-06-01 2850 0.017857143
## 67 2015-07-01 2900 0.017543860
## 68 2015-08-01 2900 0.000000000
## 69 2015-09-01 2900 0.000000000
## 70 2015-10-01 2800 -0.034482759
## 71 2015-11-01 2850 0.017857143
## 72 2015-12-01 2900 0.017543860
## 73 2016-01-01 2950 0.017241379
## 74 2016-02-01 2899 -0.017288136
## 75 2016-03-01 2950 0.017592273
## 76 2016-04-01 3000 0.016949153
## 77 2016-05-01 3000 0.000000000
## 78 2016-06-01 2900 -0.033333333
## 79 2016-07-01 2800 -0.034482759
## 80 2016-08-01 2700 -0.035714286
## 81 2016-09-01 2800 0.037037037
## 82 2016-10-01 2800 0.000000000
## 83 2016-11-01 2750 -0.017857143
## 84 2016-12-01 2700 -0.018181818
## 85 2017-01-01 2700 0.000000000
## 86 2017-02-01 2750 0.018518519
## 87 2017-03-01 2850 0.036363636
## 88 2017-04-01 2975 0.043859649
## 89 2017-05-01 3000 0.008403361
## 90 2017-06-01 3000 0.000000000
## 91 2017-07-01 2950 -0.016666667
## 92 2017-08-01 2895 -0.018644068
## 93 2017-09-01 3195 0.103626943
## 94 2017-10-01 2950 -0.076682316
## 95 2017-11-01 2950 0.000000000
## 96 2017-12-01 3000 0.016949153
## 97 2018-01-01 2950 -0.016666667
## 98 2018-02-01 3000 0.016949153gnydata<-gnydata %>% mutate(mycolor = ifelse(growth>0, "type2", "type1"))
ggplot(gnydata, aes(x=date, y=growth)) +
geom_segment( aes(x=date, xend=date, y=0, yend=growth, color=mycolor), size=1.3, alpha=0.9) +
theme_light() +
theme(
legend.position = "none",
panel.border = element_blank(),
) +
xlab("Date") +
ylab("Rent Growth Rate")## Warning: Removed 1 rows containing missing values (geom_segment).
ggplot(gnydata, aes(x=date, y=growth), group = 1) + geom_line()## Warning: Removed 1 rows containing missing values (geom_path).